The American Fintech Council (AFC) has issued a statement calling for risk-based artificial intelligence (AI) regulation in financial services.
The AFC was responding to a request from House Financial Services Committee Ranking Member Maxine Waters.
The AFC urged Congress to “pursue a unified, risk-based, and context-specific approach to AI regulation that promotes responsible innovation while protecting consumers.”
AI is already widely in use by financial services firms. This includes both internal and external services that support operations and customer needs. As the prevalence of AI increases across industries, financial services, including Fintechs, stand to benefit greatly from the technology. For consumers, access to sophisticated features, including guidance and customized services are becoming the norm.
The CEO of the AFC, Phil Goldfeder, says that AI can expand access and support fraud prevention for consumers.
“Congress has a crucial opportunity to establish a unified regulatory framework for AI use that builds on existing consumer protections while giving financial institutions the clarity they need to deploy AI responsibly.”
The AFC says that any regulation should be “risk-based and content-specific, ” as opposed to new rules that are simply based on the tech being used.
The AFC requests that there be “distinct oversight only when AI is used to create wholly new products or services. The letter also reiterates the need for a unified federal approach to AI regulation to avoid a patchwork of state requirements that could create compliance challenges and barriers to entry for smaller institutions.”
Overregulation could undermine the potential benefits while creating burdens for firms that may be harmful for smaller businesses.
Ian Moloney, Chief Policy Officer of the AFC, says:
“A thoughtful federal framework should give community financial institutions access to scalable AI tools while preserving the strong consumer protections and accountability requirements already in place.”
Some concerns voiced about AI in financial services include algorithmic bias in credit scoring, pricing, underwriting, and insurance. Historical data may skew outcomes. There is also the possibility of error and liability. And then there are “hallucinations” or AI going rogue. All of these issues are being debated within financial services and the broader AI sphere as policymakers seek to balance rules with benefits to the population.